An Image Registration Technique is already described by T. Netsch, P. Rösch, A. van Muiswinkel and J. Weese in the publication entitled “Towards real-time multi-modality 3-D medical image registration”, Computer Vision, 2001. ICCV 2001, Proceedings of Eighth IEEE International Conference, vol. 1, pp. 718-725 (2001). This cited Reference compares Medical Image Registration based on “Mutual Information” and “Local Correlation. The “Local Correlation” based method produces better or superior results to those of “Mutual Information”, and besides requires considerably fewer computations. The “Local Correlation” used in the method is a similarity measure of gray-levels within pairs of small regions, defined by “windows”, in two images to be matched together. The registration then consists in looking for the transformation of one image that will result in the largest possible value of the sum of such “Local Correlations” over pairs of corresponding windows in the two images. Different strategies are proposed in the cited publication to select the windows in each of the images being compared and to select which pairs of windows have to be compared. The choice of the strategy basically depends on the nature of transformation needed, called rigid or non-rigid transformations.
Even though results obtained with the “Local Correlation” based technique are promising, computation times are still a problem when dealing with large image data, in particular in the case of image processing in real time and in 3-D. Furthermore, robustness has to be improved in particular when dealing with non-rigid registration. To avoid matching together local shapes, which are only partly similar, the windows to compare with each other have to be large. In this case, each “Local Correlation” evaluation is computation intensive because it often involves image warping procedures requiring complex numerical algorithms.